Comparing Studies: What are the Data Saying?
Types of Studies
The study by Ong, Chua and Ng (2014) entitled “Barriers and facilitators to self-monitoring of blood glucose in people with type 2 diabetes using insulin: a qualitative study” was a qualitative study that used interviews to collect data from diabetes patients. The study by Stevens, Shi, Vane, Nie and Peters (2015) entitled “Primary care medical
home experience and health-related quality of life among adult Medicaid patients with type 2 diabetes” was a quantitative study that used surveys to collect data. The study by Wildeboer, du Pon, Schuling, Haaijer-Ruskamp and Denig (2018) was a mixed-methods study that used interviews and self-reported data to collect information on care providers’ views towards using a patient-oriented treatment decision aid when treating type 2 diabetes patients.
Statistical Tests
Statistical tests were not conducted in the qualitative study by Ong et al. (2014). Instead, thematic analysis was conducted using the comparison technique by taking the transcriptions of the interviews and identifying emergent common themes across the two dozen transcripts.
In the study by Stevens et al. (2015), a 3-point Likert-scale was used to measure responses from participants in the survey. These scores were then analyzed using descriptive statistical analysis. T-tests were conducted to establish significance between the Likert scale scores and the EQ-5D index that was also used to measure variables assessed in the study. F-statistics were also used to compare averages. Chi-squared tests were conducted to test whether there was a relationship between demographics and health measures. Finally, multivariable linear regression analysis was conducted.
In the study by Wildeboer et al. (2018), qualitative content analysis was conducted with interviews being transcribed and coded. Pearson point-biserial correlation was conducted to test associations between views of...
(2014) no statistical test was chosen because the data collected was entirely qualitative and the researchers were looking only to identify themes that could help explain the barriers and facilitators of self-management for diabetes patients.
In the study by Stevens et al. (2015), the t-test, F-statistical analysis and chi-squared tests were applicable because the researchers wanted to establish whether or not there was significance with respect to the scores they recorded, and they also wanted to test to see if there was a significant relationship between the health measures used and the demographics of the sample. These tests were chosen because they fit with the data collected and the design of the study.
In the study by Wildeboer et al. (2018), the Pearson point-biserial correlation test was conducted to test for correlation between the viewpoints provided by the care providers interviewed for the study and their intention to use the decision aid. The Pearson test was chosen because it is an appropriate test for correlation among two variables.
Differences between Parametric and Nonparametric Tests
The parametric test is a hypothesis test that is used to obtain general information that can then be used to explain the mean of the sample. A typical parametric test is the t-test. Pearson’s correlation test is another typical parametric test. Essentially, whenever an assumption about the parameters of a sample population is utilized, the parametric test is the statistical test that the researcher will engage. The measurement level of the parametric test is the interval or ratio level and the mean is the measure of central tendency.
The nonparametric test is a hypothesis test that has no assumptions built into it. It is a distribution-free approach to analyzing data. The variables that are measured here are nominal or…
